Lisnet: A Covid-19 Lung infection Segmentation Network Based On Edge Supervision and Multi-Scale Context Aggregation

Visual Place Recognition (VPR) on natural image is challenging due to the illumination variance and seasonal changes. in terms of long-term localization, the emerging event stream cameras are naturally resilient to appearance changes. in this paper, we propose a novel multi-modal network, e.g. VEFNet for VPR by learning location-specific cross RGB-event modality feature representations. Specifically, we firstly extract dense visual features via shared Convolutional Neural Network~(CNN) backbone from RGB and event frames separately. Then, two branch features are fed to the cross-modality attention module to establish correspondences between the dual-modality. We also employ a self-attention module to enhance the contextual integration within densely encoded features. Finally, the learned global descriptor is used as the place representation of the dual-modality inputs for VPR. Experimental results demonstrate the state-of-the-art~(SOTA) performance on the public datasets.